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---
license: apache-2.0
pipeline_tag: object-detection
tags:
- pytorch
- torch-dag
---
# Model Card for yolov8n_pruned_73
This is a prunned version of the [YOLOv8n](https://github.com/ultralytics/ultralytics#models) model in a [toch-dag](https://github.com/TCLResearchEurope/torch-dag) format.
This model has rougly 73% of the original model FLOPs with small metrics drop.
| Model | KMAPPs* | M Parameters | mAP50-95 (640x640) |
| ----------- | ------- | ------------ | ------------------ |
| **YOLOv8n (baseline)** | 21.5 | 3.16 | 37.3 |
| **yolov8n_pruned_73 (ours)** | 15.7 **(73%)** | 2.65 **(84%)** | 34.9 **(↓ 2.4)** |
\***KMAPPs** thousands of FLOPs per input pixel
`KMAPPs(model) = FLOPs(model) / (H * W * 1000)`, where `(H, W)` is the input resolution.
The accuracy was calculated on the COCO val2017 dataset. For details about image pre-processing, please refer to the original repository.
## Model Details
### Model Description
- **Developed by:** [TCL Research Europe](https://github.com/TCLResearchEurope/)
- **Model type:** Object detection
- **License:** Apache 2.0
- **Finetuned from model:** [YOLOv8n](https://github.com/ultralytics/ultralytics#models)
### Model Sources
- **Repository:** [YOLOv8n](https://github.com/ultralytics/ultralytics#models)
## How to Get Started with the Model
To load the model, You have to install [torch-dag](https://github.com/TCLResearchEurope/torch-dag#3-installation) library, which can be done using `pip` by
```
pip install torch-dag
```
then, clone this repository
```
# Make sure you have git-lfs installed (https://git-lfs.com)
git lfs install
git clone https://huggingface.co/TCLResearchEurope/yolov8n_pruned_73
```
and now You are ready to load the model:
```
import torch_dag
import torch
model = torch_dag.io.load_dag_from_path('./yolov8n_pruned_73')
model.eval()
out = model(torch.ones(1, 3, 224, 224))
print(out.shape)
``` |